1. Capacity and Constraint Management
PowerPoint presentation to accompany
Heizer and Render
Operations Management, 10e
Principles of Operations Management, 8e
PowerPoint slides by Jeff Heyl
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2. Process Strategies
The objective of a process strategy is to build a production process that meets customer requirements and product specifications within cost and other managerial constraints
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3. Capacity
The throughput, or the number of units a facility can hold, receive, store, or produce in a period of time
Determines fixed costs
Determines if demand will be satisfied
Three time horizons
After selection of a production process ( Chapter 7 ), managers need to determine capacity. Capacity is the “throughput,” or the number of units a facility can hold, receive, store, or produce in a given time. Capacity decisions often determine capital requirements and therefore a large portion of fixed cost. Capacity also determines whether demand will be satisfied or whether facilities will be idle. If a facility is too large, portions of it will sit unused and add cost to existing production. If a facility is too small, customers—and perhaps entire markets—will be lost. Determining facility size, with an objective of achieving high levels of utilization and a
high return on investment, is critical.
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4. Planning Over a Time Horizon
Modify capacity
Use capacity
Intermediate-range planning
Subcontract Add personnel
Add equipment Build or use inventory
Add shifts
Short-range planning
Schedule jobs
Schedule personnel
Allocate machinery *
Long-range planning
Add facilities
Add long lead time equipment
* Difficult to adjust capacity as limited options exist
Options for Adjusting Capacity
Capacity planning can be viewed in three time horizons.
Figure S7.1
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5. Design and Effective Capacity
Design capacity is the maximum theoretical output of a system
Normally expressed as a rate
Effective capacity is the capacity a firm expects to achieve given current operating constraints
Often lower than design capacity
Design capacity is the maximum theoretical output of a system in a given period under ideal conditions. It is normally expressed as a rate, such as the number of tons of steel that can be produced per week, per month, or per year. For many companies, measuring capacity can be straightforward: it is the maximum number of units the company is capable of producing in a specific time. However, for some organizations, determining capacity can be more difficult. Effective capacity is the capacity a firm expects to achieve given the current operating constraints. Effective capacity is often lower than design capacity because the facility may have
been designed for an earlier version of the product or a different product mix than is currently being produced. Table S7.1 further illustrates the relationship between design capacity, effective capacity, and actual output.
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6. Utilization and Efficiency
Utilization is the percent of design capacity achieved
Utilization = Actual output/Design capacity
Efficiency is the percent of effective capacity achieved
Two measures of system performance are particularly useful: utilization and efficiency. Utilization is simply the percent of design capacity actually achieved. Efficiency is the percent of effective capacity actually achieved. Depending on how facilities are used and managed, it may be difficult or impossible to reach 100% efficiency. Operations managers tend to be evaluated on efficiency. The key to improving efficiency is often found in correcting quality problems and in effective scheduling, training, and maintenance.
Efficiency = Actual output/Effective capacity
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7. Bakery Example Actual production last week = 148,000 rolls
Effective capacity = 175,000 rolls
Design capacity = 1,200 rolls per hour
Bakery operates 7 days/week, hour shifts
Design capacity = (7 x 3 x 8) x (1,200) = 201,600 rolls
Sara James Bakery has a plant for processing Deluxe breakfast rolls and wants to better understand its capability. Last week the facility produced 148,000 rolls. The effective capacity is 175,000 rolls. The production line operates 7 days per week, with three 8-hour shifts per day. The line was designed to process the nut-filled, cinnamon-flavored Deluxe roll at a rate of 1,200 per hour. Determine the design capacity, utilization, and efficiency for this plant when producing this Deluxe roll.
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8. Bakery Example Actual production last week = 148,000 rolls
Effective capacity = 175,000 rolls
Design capacity = 1,200 rolls per hour
Bakery operates 7 days/week, hour shifts
Design capacity = (7 x 3 x 8) x (1,200) = 201,600 rolls
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9. Bakery Example Actual production last week = 148,000 rolls
Effective capacity = 175,000 rolls
Design capacity = 1,200 rolls per hour
Bakery operates 7 days/week, hour shifts
Design capacity = (7 x 3 x 8) x (1,200) = 201,600 rolls
Utilization = 148,000/201,600 = 73.4%
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10. Bakery Example Actual production last week = 148,000 rolls
Effective capacity = 175,000 rolls
Design capacity = 1,200 rolls per hour
Bakery operates 7 days/week, hour shifts
Design capacity = (7 x 3 x 8) x (1,200) = 201,600 rolls
Utilization = 148,000/201,600 = 73.4%
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11. Bakery Example Actual production last week = 148,000 rolls
Effective capacity = 175,000 rolls
Design capacity = 1,200 rolls per hour
Bakery operates 7 days/week, hour shifts
Design capacity = (7 x 3 x 8) x (1,200) = 201,600 rolls
Utilization = 148,000/201,600 = 73.4%
Efficiency = 148,000/175,000 = 84.6%
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12. Bakery Example Actual production last week = 148,000 rolls
Effective capacity = 175,000 rolls
Design capacity = 1,200 rolls per hour
Bakery operates 7 days/week, hour shifts
Design capacity = (7 x 3 x 8) x (1,200) = 201,600 rolls I
Utilization = 148,000/201,600 = 73.4%
Efficiency = 148,000/175,000 = 84.6%
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13. Bakery Example Actual production last week = 148,000 rolls
Effective capacity = 175,000 rolls
Design capacity = 1,200 rolls per hour
Bakery operates 7 days/week, hour shifts
Efficiency = 84.6%
Efficiency of new line = 75%
Expected Output = (Effective Capacity)(Efficiency)
= (175,000)(.75) = 131,250 rolls
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14. Bakery Example Actual production last week = 148,000 rolls
Effective capacity = 175,000 rolls
Design capacity = 1,200 rolls per hour
Bakery operates 7 days/week, hour shifts
Efficiency = 84.6%
Efficiency of new line = 75%
Expected Output = (Effective Capacity)(Efficiency)
= (175,000)(.75) = 131,250 rolls
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15. Capacity Considerations
Forecast demand accurately
Understand the technology and capacity increments
Find the optimum operating level (volume)
Build for change
1. Forecast demand accurately: Product additions and deletions, competition actions, product life cycle, and unknown sales volumes all add challenge to accurate forecasting.
2. Match technology increments and sales volume: Capacity options are often constrained by technology. Some capacity increments may be large (e.g., steel mills or power plants), while others may be small (hand-crafted Louis Vuitton handbags). Large capacity increments complicate the difficult but necessary job of matching capacity to sales.
3. Find the optimum operating size (volume): Economies and diseconomies of scale often dictate an optimal size for a facility. Economies of scale exist when average cost declines as size increases, whereas diseconomies of scale occur when a larger size raises the average cost.
4. Build for change: Managers build flexibility into facilities and equipment; changes will occur in processes, as well as products, product volume, and product mix.
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16. Economies and Diseconomies of Scale
Number of Rooms 25 50 75
(dollars per room per night)
Average unit cost
25 - room roadside motel
75 - room roadside motel
50 - room roadside motel
Economies and diseconomies of scale often dictate an optimal size for a facility. Economies of scale exist when average cost declines as size increases, whereas diseconomies of scale occur when a larger size raises the average cost. As Figure S7.2 suggests, most businesses have an optimal size—at least until someone
comes along with a new business model. For decades, very large integrated steel mills were considered optimal.
Economies of scale
Diseconomies of scale
Figure S7.2
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17. Managing Demand Demand exceeds capacity Capacity exceeds demand
Curtail demand by raising prices, scheduling longer lead time
Long term solution is to increase capacity
Capacity exceeds demand
Stimulate market
Product changes
Adjusting to seasonal demands
Produce products with complementary demand patterns
Even with good forecasting and facilities built to accomodate that forecast, there may be a poor match between the actual demand that occurs and available capacity. A poor match may mean demand exceeds capacity or capacity exceeds demand. However, in both cases, firms have options.
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18. Complementary Demand Patterns
4,000 –
3,000 –
2,000 –
1,000 –
J F M A M J J A S O N D J F M A M J J A S O N D J
Sales in units
Time (months)
Jet ski engine sales
Figure S7.3
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19. Complementary Demand Patterns
4,000 –
3,000 –
2,000 –
1,000 –
J F M A M J J A S O N D J F M A M J J A S O N D J
Sales in units
Time (months)
Snowmobile motor sales
Jet ski engine sales
Figure S7.3
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20. Complementary Demand Patterns
Combining both demand patterns reduces the variation
4,000 –
3,000 –
2,000 –
1,000 –
J F M A M J J A S O N D J F M A M J J A S O N D J
Sales in units
Time (months)
Snowmobile motor sales
Jet ski engine sales
Adjusting to Seasonal Demands A seasonal or cyclical pattern of demand is another capacity challenge. In such cases, management may find it helpful to offer products with complementary demand patterns—that is, products for which the demand is high for one when low for the other. For example, in Figure S7.3 the firm is adding a line of snowmobile motors to its line of jet skis to smooth demand.
Figure S7.3
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21. Tactics for Matching Capacity to Demand
Making staffing changes
Adjusting equipment
Purchasing additional machinery
Selling or leasing out existing equipment
Improving processes to increase throughput
Redesigning products to facilitate more throughput
Adding process flexibility to meet changing product preferences
Closing facilities
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22. Demand and Capacity Management in the Service Sector
Demand management
Appointment, reservations, FCFS rule
Capacity management
Full time, temporary, part-time staff
Demand Management: When demand and capacity are fairly well matched, demand management can often be handled with appointments, reservations, or a first-come, firstserved rule. In some businesses, such as doctors’ and lawyers’ offices, an appointment system is the schedule and is adequate. Reservations systems work well in rental car agencies, hotels, and some restaurants as a means of minimizing customer waiting time and avoiding disappointment over unfilled service. In retail shops, a post office, or a fast-food restaurant, a first-come, first-served rule for serving customers may suffice. Capacity Management When managing demand is not feasible, then managing capacity through changes in full-time, temporary, or part-time staff may be an option. This is the approach in many services. For instance, hospitals may find capacity limited by a shortage of board-certified radiologists willing to cover the graveyard shifts. Getting fast and reliable radiology
readings can be the difference between life and death for an emergency room patient.
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23. Break-Even Analysis
Technique for evaluating process and equipment alternatives
Objective is to find the point in dollars and units at which cost equals revenue
Requires estimation of fixed costs, variable costs, and revenue
Break-even analysis is the critical tool for determining the capacity a facility must have to achieve profitability. The objective of break-even analysis is to find the point, in dollars and units, at which costs equal revenue. This point is the break-even point. Firms must operate above this level to achieve profitability.
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24. Break-Even Analysis
Fixed costs are costs that continue even if no units are produced
Depreciation, taxes, debt, mortgage payments
Variable costs are costs that vary with the volume of units produced
Labor, materials, portion of utilities
Contribution is the difference between selling price and variable cost
Break-even analysis is the critical tool for determining the capacity a facility must have to achieve profitability. The objective of break-even analysis is to find the point, in dollars and units, at which costs equal revenue. This point is the break-even point. Firms must operate above this level to achieve profitability.
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25. Break-Even Analysis Assumptions Costs and revenue are linear functions
Generally not the case in the real world
We actually know these costs
Very difficult to verify
Time value of money is often ignored
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26. Total cost = Total revenue
Break-Even Analysis –
900 –
800 –
700 –
600 –
500 –
400 –
300 –
200 –
100 –
| | | | | | | | | | | |
Cost in dollars
Volume (units per period)
Total revenue line
Profit corridor
Loss corridor
Total cost line
Break-even point
Total cost = Total revenue
Variable cost
Another element in break-even analysis is the revenue function . In Figure S7.5 , revenue begins at the origin and proceeds upward to the right, increasing by the selling price of each unit. Where the revenue function crosses the total cost line (the sum of fixed and variable costs) is the break-even point, with a profit corridor to the right and a loss corridor to the left.
Fixed cost
Figure S7.5
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27. Break-Even Analysis TR = TC F or BEPx = P - V Px = F + Vx
BEPx = break-even point in units
BEP$ = break-even point in dollars
P = price per unit (after all discounts)
x = number of units produced
TR = total revenue = Px
F = fixed costs
V = variable cost per unit
TC = total costs = F + Vx
Break-even point occurs when
TR = TC or
Px = F + Vx
BEPx = F
P - V
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28. Break-Even Analysis BEP$ = BEPx P = P = Profit = TR - TC P - V
BEPx = break-even point in units
BEP$ = break-even point in dollars
P = price per unit (after all discounts)
x = number of units produced
TR = total revenue = Px
F = fixed costs
V = variable cost per unit
TC = total costs = F + Vx
BEP$ = BEPx P
= P = F
(P - V)/P
P - V
1 - V/P
Profit = TR - TC
= Px - (F + Vx)
= Px - F - Vx
= (P - V)x - F
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29. Break-Even Example Fixed costs = $10,000 Material = $.75/unit
Direct labor = $1.50/unit Selling price = $4.00 per unit
BEP$ = = F
1 - (V/P)
$10,000
1 - [( )/(4.00)]
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30. Break-Even Example Fixed costs = $10,000 Material = $.75/unit
Direct labor = $1.50/unit Selling price = $4.00 per unit
BEP$ = = F
1 - (V/P)
$10,000
1 - [( )/(4.00)]
= = $22,857.14
$10,000
.4375
Stephens, Inc., wants to determine the minimum dollar volume and unit volume needed at its new facility to break even.
BEPx = = = 5,714 F
P - V
$10,000
( )
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31. Break-Even Example Revenue Break-even point Total costs Fixed costs
50,000 –
40,000 –
30,000 –
20,000 –
10,000 – –
| | | | | |
0 2,000 4,000 6,000 8,000 10,000
Dollars
Units
Revenue
Break-even point
Total costs
Fixed costs
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32. ∑ 1 - x (Wi) Break-Even Example Multiproduct Case F BEP$ = Vi Pi
Most firms, from manufacturers to restaurants, have a variety of offerings. Each offering may have a different selling price and variable cost. Utilizing break-even analysis, we modify Equation (S7-4) to reflect the proportion of sales for each product. We do this by “weighting” each product’s contribution by its proportion of sales.
where V = variable cost per unit
P = price per unit
F = fixed costs
W = percent each product is of total dollar sales
i = each product
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33. Multiproduct Example Fixed costs = $3,000 per month Annual Forecasted
Item Price Cost Sales Units
Sandwich $5.00 $3.00 9,000
Drink ,000
Baked potato ,000
Le Bistro, like most other resturants, makes more than one product and would like to know its breakeven point in dollars. Information for Le Bistro follows. Fixed costs are $3,000 per month.
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34. Multiproduct Example Fixed costs = $3,000 per month Annual Forecasted
Item Price Cost Sales Units
Sandwich $5.00 $3.00 9,000
Drink ,000
Baked potato ,000
Sandwich $5.00 $ $45,
Drinks ,
Baked ,
potato
$72,
Annual Weighted
Selling Variable Forecasted % of Contribution
Item (i) Price (P) Cost (V) (V/P) 1 - (V/P) Sales $ Sales (col 5 x col 7)
With a variety of offerings, we proceed with break-even analysis just as in a single-product case, except that we weight each of the products by its proportion of total sales using Equation (S7-5) . Note: Revenue for sandwiches is $45,000 ( * 9,000 ), which is 62.1% of the total revenue of $72,500. Therefore, the contribution for sandwiches is “weighted” by The weighted contribution is .621 * .40 = In this manner, its relative contribution is properly reflected.
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35. Multiproduct Example ∑ 1 - x (Wi) F BEP$ = Vi Pi
= = $76,759
$3,000 x 12
.469
Fixed costs = $3,000 per month
Annual Forecasted
Item Price Cost Sales Units
Sandwich $5.00 $3.00 9,000
Drink ,000
Baked potato ,000
Daily sales
= = $246.02
$76,759
312 days
Sandwich $5.00 $ $45,
Drinks ,
Baked ,
potato
$72,
Annual Weighted
Selling Variable Forecasted % of Contribution
Item (i) Price (P) Cost (V) (V/P) 1 - (V/P) Sales $ Sales (col 5 x col 7)
.621 x $246.02
$5.00
= 30.6  31
sandwiches
per day
Using this approach for each product, we find that the total weighted contribution is .47 for each dollar of sales, and the break-even point in dollars is $76,596:
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36. Reducing Risk with Incremental Changes
(a) Leading demand with incremental expansion
Demand
Expected demand
New capacity
(b) Capacity lags demand with incremental expansion
Demand
New capacity
Expected demand
(c) Attempts to have an average capacity with incremental expansion
Demand
New capacity
Expected demand
Demand growth is usually in small units, while capacity additions are likely to be both instantaneous and in large units. This contradiction adds to the capacity decision risk. To reduce risk, incremental changes that hedge demand forecasts may be a good option. Figure S7.6 illustrates four approaches to new capacity.
Figure S7.6
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37. Reducing Risk with Incremental Changes
(a) Leading demand with incremental expansion
Demand
Time (years) 1 2 3
New capacity
Expected demand
Alternative Figure S7.6 (a) leads capacity—that is, acquires capacity to stay ahead of demand, with new capacity being acquired at the beginning of period 1. This capacity handles increased demand until the beginning of period 2. At the beginning of period 2, new capacity is again acquired, allowing the organization to stay ahead of demand until the beginning of period 3. This process can be continued indefinitely into the future. Here capacity is acquired incrementally —at the beginning of period 1 and at the beginning of period 2.
Figure S7.6
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38. Reducing Risk with Incremental Changes
(b) Capacity lags demand with incremental expansion
Demand
Time (years) 1 2 3
New capacity
Expected demand
But managers can also elect to make a larger increase at the beginning of period 1 [ Figure S7.6 (b)]—an increase that may satisfy expected demand until the beginning of period 3. Excess capacity gives operations managers flexibility. For instance, in the hotel industry, added (extra) capacity in the form of rooms can allow a wider variety of room options and perhaps flexibility in room cleanup schedules. In manufacturing, excess capacity can be used to do more setups, shorten production runs, and drive down inventory costs.
Figure S7.6
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39. Reducing Risk with Incremental Changes
(c) Attempts to have an average capacity with incremental expansion
Demand
Time (years) 1 2 3
New capacity
Expected demand
Figure S7.6 (c) shows an option that lags capacity, perhaps using overtime or subcontracting to accommodate excess demand. Finally, Figure S7.6 (d) straddles demand by building capacity that is “average,” sometimes lagging demand and sometimes leading it. Both the lag and straddle option have the advantage of delaying capital expenditure.
Figure S7.6
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40. Expected Monetary Value (EMV) and Capacity Decisions
Determine states of nature
Future demand
Market favorability
Analyzed using decision trees
Hospital supply company
Four alternatives
To present a manager’s decision alternatives, we can develop decision trees using the above symbols. When constructing a decision tree, we must be sure that all alternatives and states of nature are in their correct and logical places and that we include all possible alternatives and states of nature.
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41. Expected Monetary Value (EMV) and Capacity Decisions
-$90,000
Market unfavorable (.6)
Market favorable (.4)
$100,000
Large plant
Market favorable (.4)
Market unfavorable (.6)
$60,000
-$10,000
Medium plant
Market favorable (.4)
Market unfavorable (.6)
$40,000
-$5,000
Small plant $0
Do nothing
Getz Products Company is investigating the possibility of producing and marketing backyard storage sheds. Undertaking this project would require the construction of either a large or a small manufacturing plant. The market for the product produced—storage sheds—could be either favorable or unfavorable. Getz, of course, has the option of not developing the new product line at all.
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42. Expected Monetary Value (EMV) and Capacity Decisions
-$90,000
Market unfavorable (.6)
Market favorable (.4)
$100,000
Large plant
Market favorable (.4)
Market unfavorable (.6)
$60,000
-$10,000
Medium plant
Market favorable (.4)
Market unfavorable (.6)
$40,000
-$5,000
Small plant $0
Do nothing
Large Plant
EMV = (.4)($100,000) + (.6)(-$90,000)
EMV = -$14,000
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43. Expected Monetary Value (EMV) and Capacity Decisions
-$14,000
-$90,000
Market unfavorable (.6)
Market favorable (.4)
$100,000
Large plant
$18,000
Market favorable (.4)
Market unfavorable (.6)
$60,000
-$10,000
Medium plant
Market favorable (.4)
Market unfavorable (.6)
$40,000
-$5,000
Small plant $0
Do nothing
$13,000
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